NURS FPX 4045 Assessment 3 Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

NURS FPX 4045 Assessment 3

Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

Student name

Capella University

NURS-FPX4045 Nursing Informatics: Managing Health Information and Technology

Professor Name

Submission Date

Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing

One of the most effective technologies in diabetes management that provides real-time glucose level monitoring is Continuous Glucose Monitoring (CGM). In the case of Sentinel U Telehealth Nursing Simulation -William Townsend, CGM, was the primary tool for managing a patient with type 2 diabetes, hypertension, and obesity. The device has an interstitial glucose sensor when implanted beneath the skin and transfers the data wirelessly to a telehealth platform or a smartphone (Su et al., 2025). The relationship will enable nurses to track trends, receive automatic alerts, and intervene before glucose extremes.

Nursing-wise, CGM will have a direct influence on patient safety and quality outcomes as it will detect hypo- or hyperglycemia as early as possible and offer evidence-based interventions. It will support patient education and adherence, the self-management process, and reduce the number of visits by patients. Combined with telehealth, CGM will enable nurses to track diverse patients and deliver care according to risk. Along with the growing digital health, this paper explains that CGM can be used to enhance nursing practice, patient engagement, and care coordination when paired with artificial intelligence (AI).

Rationale for Selecting This Technology Topic

CGM was chosen because it was one of the major areas in the Sentinel U William Townsend simulation and reflects the shift to data-driven and technology-assisted nursing. Telehealth-based chronic-disease care is essential as the system can exchange real-time information on glucose levels at the time between clinicians and patients. This coincides with the goals of Healthy People 2030 of reducing the burden of chronic illnesses and improving equitable access through online intervention (Wong et al., 2021). CGM is patient-centered care, and hence continuous feedback loops to drive timely nursing decisions as well as enable patients to manage themselves. Its characteristics of safety, compliance, and education have been established to make it an evidence-based technology that improves the presence of nurses in care environments.

• Research Process

The sources included in the systematic search were CINAHL, PubMed/MEDLINE, Cochrane Library, IEEE Xplore, and JMIR using the following keywords: continuous glucose monitoring, remote patient monitoring, telehealth, artificial intelligence, diabetes management, and quality of care. Inclusion criteria were English, peer-reviewed (2021-2025), sample of adult patients with T2DM, and the study should provide CGM-related outcomes. Eight articles were found, and five of them were directly identified based on the methodological rigor, the applicability of telehealth, and its relevance to nursing. The evaluation is based on the input provided by CGM in the areas of safety, quality, and patient involvement to be used as the basis of the articles.

Annotated Bibliography

• Integration of Artificial Intelligence with Continuous Glucose Monitoring

Ji, C., Jiang, T., Liu, L., Zhang, J., & You, L. (2025). Continuous glucose monitoring combined with artificial intelligence: Redefining the pathway for prediabetes management. Frontiers in Endocrinology, 16(1). https://doi.org/10.3389/fendo.2025.1571362

Another concept introduced by Ji et al. (2025) is the possibility of using the concept of artificial intelligence (AI) along with Continuous Glucose Monitoring (CGM) to enhance the management of diabetes and prediabetes. The article states that the data on glucose patterns of CGM devices is then handled by AI algorithms, which predict the change and send an automatic alarm to patients and health care providers. The AI-CGM system allows adjusting the diet, lifestyle, and medication on time through the help of predictive modeling to prevent an unsafe level of glucose. As per the research, AI improves the quality of CGM, analyzes large data volumes, and tailors care to reduce the chance of developing complications. In the case of telehealth nurses, technology would help to achieve remote triage-effective intervention, improve safety, reduce hospital visits, and promote self-management in patients. Comprehensively, the research results suggest the significance of AI analytics implementation in telemonitoring to facilitate evidence-based nursing and the utility of predictive analytics in enhancing the quality and efficiency of diabetes management in the virtual environment.

• Real-World Outcomes and Safety Benefits of CGM-Based Digital Health Solutions

Kumbara, A. B., Iyer, A. K., Green, C. R., Jepson, L. H., Leone, K., Layne, J. E., & Shomali, M. (2023). Impact of a combined continuous glucose monitoring–digital health solution on glucose metrics and self-management behavior for adults with type 2 diabetes: Real-world, observational study. JMIR Diabetes, 8(1). https://doi.org/10.2196/47638

To evaluate the impact of the Continuous Glucose Monitoring (CGM) and a digital health app as a combination on self-management and glucose levels in Type 2 Diabetes patients, Kumbara et al. (2023) used a real-life observational study. The respondents using CGM on a mobile platform reported high improvements in time-in-range (TIR) of the glucose level and low rates of hypoglycemia, the outcome of which demonstrates better clinical safety. Other aspects of intervention that promoted regular self-care behavior, such as following the diet, taking medicine, and exercising, were also promoted. The findings indicate that CGM, telehealth, and digital coaching may be applied to optimize patient outcomes and the quality of care, and decrease the number of complications that can be avoided. The study is compatible with the nursing model and will show how nurses are significant in the interpretation of the CGM readings, educational guidance, and behavioral modification assistance through the online services.

• Enhancing Quality of Care and Interdisciplinary Collaboration through Digital Health Integration

Maida, E., Caruso, P., Bonavita, S., Abbadessa, G., Miele, G., Longo, M., Scappaticcio, L., Ruocco, E., Trojsi, F., Esposito, K., Lavorgna, L., & Maiorino, M. I. (2025). Digital health in diabetes care: A narrative review from monitoring to the management of systemic and neurologic complications. Journal of Clinical Medicine, 14(12), 4240. https://doi.org/10.3390/jcm14124240

Maida et al. (2025) is a narrative review that explores how the management of diabetes and prevention of complications can be achieved using Continuous Glucose Monitoring (CGM), teleconsultations, and mobile health apps, digital health technologies. The authors demonstrate that with the help of digital platforms, it is possible to monitor and early detect comorbidities and to enhance the collaboration between nurses, endocrinologists, dietitians, and neurologists. The incorporation of CGM into telehealth systems enhances care coordination, patient compliance, and patient clinical decision-making. Patient empowerment via digital literacy and self-management tools to promote shared decision-making and autonomy is also highlighted in the review. In the context of nursing practice, it demonstrates how the telehealth-based CGM interventions can be applied in keeping with the principles of nursing informatics, enhancing communication, workflow, and education. All in all, the research shows that telemonitoring, remote consultation, and real-time data sharing contribute to the quality of care and the interdisciplinary collaboration that is the key to effective and ethical telehealth. It is applicable in the section on quality of care and interdisciplinary collaboration because this article has connected technological innovation and collaborative nursing practice.

• Evidence for Telehealth Effectiveness in Diabetes Management

Ravi, S., Meyerowitz-Katz, G., Yung, C., Ayre, J., McCaffery, K., Maberly, G., & Bonner, C. (2025). Effect of virtual care in type 2 diabetes management – A systematic umbrella review of systematic reviews and meta-analysis. BMC Health Services Research, 25(1). https://doi.org/10.1186/s12913-025-12496-0

Ravi et al. (2025) conducted a systematic umbrella review, which summarized information regarding different meta-analyses that evaluated the impact of virtual and telehealth interventions on Type 2 Diabetes (T2D) outcomes. Experts have found that virtual models of care (which comprised Continuous Glucose Monitoring (CGM), mobile health, and distance visits) made an enormous difference in terms of lowering the HbA1c levels, improving the medication adherence, and lowering the number of hospitalizations. The applicability of the study findings can be empirically used to prove the relevance of the telehealth integration in the management of chronic diseases, with a particular interest in the enhancement of glycemic control and patient outcomes, in general. It is also interesting to note that the review shows the importance of healthcare providers, including nurses, in keeping patients engaged in virtual communication and feedback. These results help to support the clinical relevance of CGM in a broader digital health care setting and its use in evidence-based, outcome-focused care in the case of telehealth nurses.

• Artificial Intelligence Applications in Continuous Glucose Monitoring and Diabetes Care

Parab, R., Feeley, J. M., Valero, M., Chadalawada, L., Garcia, G.-G. P., Kar, S. S., Madabhushi, A., Breton, M. D., Li, J., Shao, H., & Pasquel, F. J. (2025). Artificial intelligence in diabetes care: Applications, challenges, and opportunities ahead. Endocrine Practice, 25(1), 00966-8. https://doi.org/10.1016/j.eprac.2025.07.008

The entire list of ways of artificial intelligence (AI) application to the nondiabetic situation is given by Parab et al. (2025), yet it focuses on how the machine learning models and predictive algorithms could change the Continuous Glucose Monitors (CGM) systems. The authors inform about the AI-based CGM systems, which can process the large volumes of glucose data to simplify the insulin titration process, identify the glycemic variability, and forecast the hypoglycemic or hyperglycemic events with high accuracy. The advantages of this capability are that it helps in early intervention, promotes patient safety, and makes the planning of treatment very personal. Furthermore, the paper also discerns the issues of the biased restrictions, data secrecy, and necessity of clinical supervision- all the aspects that will be required to be taken into account to introduce the integration into the clinical environment safely. These understandings make telehealth nursing a transformative concept of the nursing profession as information decoders of AI-enabled information and educators on technology-assisted care to patients. The article advances the fact that the process of clinical decision-making can be altered, and the workflow will be more efficient, and the scope of nursing-related assessment can be expanded with the help of AI in remote diabetes management. The source is also a nice complement to Ji et al. (2025) and offers good evidence on the behalf of AI Integration as it demonstrates how AI may lead to improvement of the predictive and therapeutic abilities of CGM.

Artificial Intelligence Integration

AI-enhanced CGM devices are a combination of predictive analytics and real-time glucose-reading to provide preventive and personalized treatment. The machine-learning algorithms compare the tendencies of the data, draw the predictions, and provide alert messages, which, as reported by Ji et al. (2025) and Parab et al. (2025), may be automated to alert the nurse to act before the complications occur. The insights would be capable of assisting the nurses with patient education and remote triage, and improved plans of care. AI is correct, and nurses ensure that AI is ethically applied by maintaining privacy, transparency, and clinical validation. This technological admixture of nursing judgment is one of the examples of evidence-based and patient-centered innovation.

Organized Factors and Implementation Justification

Implementation of the CGM-telehealth programs needs organizational preparation and compliance with the privacy and interoperability requirements. HIPAA-compliant data-sharing, effortless integration of EHR, and cloud storage protect patient data. Training of the employees will be required to prepare them to install devices, troubleshoot, and coach them remotely. Remote-monitoring systems present a major advantage of compliance and cost-effectiveness in case of the system is supported by effective training and cybersecurity measures (Tan et al., 2024). Su et al. (2025) also include that the problem of attrition and the level of digital literacy could be addressed by personalizing the onboarding and offering user-friendly interfaces. The leadership should take equity into account since they provide low-cost devices and broadband services to the underserved individuals. These, together with organizational measures, will ensure safe, sustainable, and ethical implementation of CGM in telehealth nursing.

Summary of the Recommendation

Integrating CGM, telehealth, and AI analytics would increase patient safety, outcomes, and engagement. The evidence offered by Ji et al. (2025), Kumbara et al. (2023), and Ravi et al. (2025) confirms the following: reduced hypoglycemia, improved HbA1c, increased time-in-range, and reduced readmission. Nurse-led CGM-telehealth models are capable of facilitating proactive care planning and real-time education to facilitate self-management. This plan directly focuses on the goals of Healthy People 2030, to facilitate of prevention of chronic conditions and equity through digital innovation. These technologies also enhance the competencies that are associated with nursing informatics, the interpretation of the data, the use of AI ethically, and interprofessional collaboration, which lie at the heart of modern evidence-based practice.

Conclusion

One of the pillars of modern chronic-disease management is Continuous Glucose Monitoring, which is reinforced by AI and telehealth. It encourages collaboration among nurses and patients, preventive intervention, and safety and access. CGM provides practical data that enhances evaluation, training, and practice in the case of nursing. Together, these instruments guarantee the dream of evidence-based patient-centered care.

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